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Peer-reviewedCC BY 4.0Frontiers in Immunology

A multicohort proteomics-based machine learning score (MASHPredict) for noninvasive diagnosis and long-term prognostication of metabolic dysfunction-associated steatohepatitis

  • Xu Hu1
  • Fanlai Meng2
  • Wei Zhou1,3
  • Shun Li4
  • Yujie Gao1
  • Yali Zhang1
  • Jie Zhang1
  • Lianhua Kong5
  • Guokun Liu6
  • Xiaohong Shi2
  • Jianbo Xu7
  • Qing Zhang8
1 more authors
  • Wen Hu1
8 affiliations
  1. 1Department of Endocrinology and Metabolism/Clinical Nutrition, the Affiliated Huai’an Hospital of Xuzhou Medical University
  2. 2Department of Pathology, Nanjing Drum Tower Hospital Group Suqian Hospital
  3. 3Department of Endocrinology and Metabolism, Huai’an 82 Hospital
  4. 4Department of Neurology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Medical School, Nanjing University
  5. 5Department of Medical Insurance, the First Affiliated Hospital of Nanjing Medical University
  6. 6Department of Nursing, the Affiliated Huai’an Hospital of Xuzhou Medical University
  7. 7Department of Hepatobiliary Surgery, the Affiliated Huai’an NO.1 People’s Hospital of Nanjing Medical University
  8. 8Department of Hepatology, Huai’an No. 4 People’s Hospital
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Abstract

Background: Metabolic dysfunction-associated steatohepatitis (MASH) can progress to cirrhosis and hepatocellular carcinoma, yet the invasiveness of liver biopsy limits its clinical utility. Accurate noninvasive diagnostic tools are essential for improved patient risk stratification.

Methods: A machine learning study was conducted using plasma proteomics from 142 participants (83 MASH cases, 59 controls) recruited from a Chinese hospital, randomly divided into training and validation cohorts (7:3 ratio). Key proteins were identified via feature selection using Least Absolute Shrinkage and Selection Operator regression and the Boruta algorithm. Ten machine learning models were developed to create the MASHPredict score, with external validation in two test cohorts. Prognostic evaluation focused on liver-related events, atherosclerotic cardiovascular disease (ASCVD)-related mortality, and all-cause mortality.

Results: MASHPredict demonstrated robust diagnostic performance, with Area Under the Curve (AUC) values of 0.83 (95% CI: 0.74–0.92) in the training cohort, 0.80 (0.66–0.94) in the validation cohort, 0.79 (0.67–0.91) in test cohort 1, and 0.79 (0.76–0.83) in test cohort 2. It outperformed established noninvasive tests, including the Fibrosis-4 Index, AST to Platelet Ratio Index, AST/ALT Ratio, and NAFLD Fibrosis Score. High-risk individuals exhibited significantly increased hazards for liver-related mortality (HR = 8.18), MASH (HR = 12.07), cirrhosis (HR = 3.37), liver cancer (HR = 3.11), ASCVD-related mortality (HR = 1.64), and all-cause mortality (HR = 1.42) (all p < 0.001).

Conclusion: The MASHPredict score is a validated, noninvasive diagnostic tool with broad applicability. Its superior performance in diagnosing MASH and prognosticating liver-related outcomes supports its potential role in early detection, risk stratification, and clinical decision-making.

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Cite

BibTeXRIS
BibTeX
@article{hu2026multicohort,
  title = {{A multicohort proteomics-based machine learning score (MASHPredict) for noninvasive diagnosis and long-term prognostication of metabolic dysfunction-associated steatohepatitis}},
  author = {Xu Hu and Fanlai Meng and Wei Zhou and Shun Li and Yujie Gao and Yali Zhang and Jie Zhang and Lianhua Kong and Guokun Liu and Xiaohong Shi and Jianbo Xu and Qing Zhang and Wen Hu},
  journal = {Frontiers in Immunology},
  year = {2026},
  month = oct,
  doi = {10.3389/fimmu.2026.1849699},
  url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1849699/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A multicohort proteomics-based machine learning score (MASHPredict) for noninvasive diagnosis and long-term prognostication of metabolic dysfunction-associated steatohepatitis
AU  - Xu Hu
AU  - Fanlai Meng
AU  - Wei Zhou
AU  - Shun Li
AU  - Yujie Gao
AU  - Yali Zhang
AU  - Jie Zhang
AU  - Lianhua Kong
AU  - Guokun Liu
AU  - Xiaohong Shi
AU  - Jianbo Xu
AU  - Qing Zhang
AU  - Wen Hu
PY  - 2026
DA  - 2026/10/15
JO  - Frontiers in Immunology
DO  - 10.3389/fimmu.2026.1849699
UR  - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1849699/full
AB  - Background: Metabolic dysfunction-associated steatohepatitis (MASH) can progress to cirrhosis and hepatocellular carcinoma, yet the invasiveness of liver biopsy limits its clinical utility. Accurate noninvasive diagnostic tools are essential for improved patient risk stratification.  Methods: A machine learning study was conducted using plasma proteomics from 142 participants (83 MASH cases, 59 controls) recruited from a Chinese hospital, randomly divided into training and validation cohorts (7:3 ratio). Key proteins were identified via feature selection using Least Absolute Shrinkage and Selection Operator regression and the Boruta algorithm. Ten machine learning models were developed to create the MASHPredict score, with external validation in two test cohorts. Prognostic evaluation focused on liver-related events, atherosclerotic cardiovascular disease (ASCVD)-related mortality, and all-cause mortality.  Results: MASHPredict demonstrated robust diagnostic performance, with Area Under the Curve (AUC) values of 0.83 (95% CI: 0.74–0.92) in the training cohort, 0.80 (0.66–0.94) in the validation cohort, 0.79 (0.67–0.91) in test cohort 1, and 0.79 (0.76–0.83) in test cohort 2. It outperformed established noninvasive tests, including the Fibrosis-4 Index, AST to Platelet Ratio Index, AST/ALT Ratio, and NAFLD Fibrosis Score. High-risk individuals exhibited significantly increased hazards for liver-related mortality (HR = 8.18), MASH (HR = 12.07), cirrhosis (HR = 3.37), liver cancer (HR = 3.11), ASCVD-related mortality (HR = 1.64), and all-cause mortality (HR = 1.42) (all p < 0.001).  Conclusion: The MASHPredict score is a validated, noninvasive diagnostic tool with broad applicability. Its superior performance in diagnosing MASH and prognosticating liver-related outcomes supports its potential role in early detection, risk stratification, and clinical decision-making.
SN  - 1664-3224
ER  -